RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
This addresses the need for both accurate and interpretable predictive models in medicine, particularly for EHR data, though it is an incremental improvement combining existing attention mechanisms with a reverse time order.
The authors tackled the trade-off between accuracy and interpretability in predictive models for healthcare by developing RETAIN, a model that uses a reverse time attention mechanism on EHR data, achieving predictive accuracy comparable to state-of-the-art RNNs while maintaining interpretability similar to traditional models.
Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are important. We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention. RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.